Classification Algorithm on Raisin Dataset

Ni Wayan Parwati Septiani; Toto Haryanto

Ni Wayan Parwati Septiani Program Studi Teknik Informatika Universitas Indrapasta PGRI
Toto Haryanto IPB University

Keywords: Classification, Decision Tree, k-Nearest Neighbor, Naïve Bayes, Raisin Data

Abstract

Raisins are dried grapes that are consumed globally and contain components that are helpful to human health. Research indicates that raisins can reduce the risk of heart disease by lowering blood sugar and blood pressure. This paper will focus on simple classifier, there are Gaussian Naïve Bayes, Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Decision Tree, and k-Nearest Neighbor classifier to classify raisin (kecimen or Besni). Among those three algorithms, k-Nearest Neighbor (k = 3) accuracy outperforms the other two classification algorithms. It resulted an accuracy 86,7%.

References

1, A. O.-C. D. E.-A., Pérez, A. J., Marhuenda-Muñoz, M., Lamuela-Raventós, R. M., & Vallverdú-Queralt, A. (2020). The Facts on Raisins & Dried Fruits. Nutrients, 12(1).
Ali, A., Alrubei, M., Hassan, L. F. M., Al-Ja’afari, M., & Abdulwahed, S. (2020). Diabetes classification based on KNN. IIUM Engineering Journal, 21(1). https://doi.org/10.31436/iiumej.v21i1.1206
Altheneyan, A. S., & Menai, M. E. B. (2014). Naïve Bayes classifiers for authorship attribution of Arabic texts. Journal of King Saud University - Computer and Information Sciences, 26(4). https://doi.org/10.1016/j.jksuci.2014.06.006
Ampomah, E. K., Nyame, G., Qin, Z., Addo, P. C., Gyamfi, E. O., & Gyan, M. (2021). Stock market prediction with gaussian naïve bayes machine learning algorithm. Informatica (Slovenia), 45(2). https://doi.org/10.31449/inf.v45i2.3407
Bayes, T. (1991). An essay towards solving a problem in the doctrine of chances. 1763. M.D. Computing : Computers in Medical Practice, 8(3). https://doi.org/10.1093/biomet/45.3-4.296
Cagliero, L., & Garza, P. (2013). Improving classification models with taxonomy information. Data and Knowledge Engineering, 86. https://doi.org/10.1016/j.datak.2013.01.005
Chen, C., Geng, L., & Zhou, S. (2021). Design and implementation of bank CRM system based on decision tree algorithm. Neural Computing and Applications, 33(14). https://doi.org/10.1007/s00521-020-04959-8
ÇINAR, İ., KOKLU, M., & TAŞDEMİR, Ş. (2020). Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods. Gazi Journal of Engineering Sciences, 6(3), 200–209.
Cunningham, P., & Delany, S. J. (2021). K-Nearest Neighbour Classifiers-A Tutorial. In ACM Computing Surveys (Vol. 54, Issue 6). https://doi.org/10.1145/3459665
Granik, M., & Mesyura, V. (2017). Fake news detection using naive Bayes classifier. 2017 IEEE 1st Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 - Proceedings. https://doi.org/10.1109/UKRCON.2017.8100379
Jiawei, H., Micheline, K., & Jian, P. (2012). DATA MINING (Concept and Techniques). In DATA MINING (Vol. 3, Issue 13). https://doi.org/10.1017/CBO9781107415324.004
Lange, N., Bishop, C. M., & Ripley, B. D. (1997). Neural Networks for Pattern Recognition. Journal of the American Statistical Association, 92(440). https://doi.org/10.2307/2965437
Rokach, L., & Maimon, O. (2010). The Data Mining and Knowledge Discovery Handbook - sid 1203-1224 XXXXX. In The Data Mining and Knowledge Discovery Handbook.
Singh, M., Wasim Bhatt, M., Bedi, H. S., & Mishra, U. (2020). Performance of bernoulli’s naive bayes classifier in the detection of fake news. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.896
Xu, S. (2018). Bayesian Naïve Bayes classifiers to text classification. Journal of Information Science, 44(1). https://doi.org/10.1177/0165551516677946